To improve efficiency of medical practitioners and to curtail the rising cost of providing health care, many attempts have been made to use computers or electronic medical records (EMR) systems to facilitate the delivery of a variety of health care services. Such systems have generally been poorly integrated with the medical practitioner's workflow and have not been widely adopted.
Medical practitioners generally spend most of their medical practice workday servicing patients. Over time, most medical practitioners have derived a process of the medical practitioner-patient encounter (“the Physical Exam”) that divides it into two main discrete parts, with each part either produces or employs specific health-related information. Such health-related information generally falls into two information segments: anatomical and diagnostic. Typically, anatomical and diagnostic information includes: historical health information, including symptoms described by the patient; physical examination observations, including objective findings by the medical practitioner; assessment, including diagnosis, differential diagnosis, working diagnosis; and plan, which may include: diagnostic or treatment procedure; scheduling of procedures, referral and/or reassessment; information/education for patient; projected care plan and other processes and functions appropriate to each given diagnosis for a particular patient.
For a diagnosis, in the process and flow of the Physical exam, both anatomical and diagnostic information may be utilized to generate and record one or more medical findings. Such medical findings are generally documented for later review, but are also created as a result of the Physical exam and are used to initiate one or more procedures that result from the physical exam.
The health-related information processed in the Physical exam either generates or employs descriptive health-related data which can be classified into two groups: “anatomical data,” such as historical data and physical examination findings, and “diagnostic data,” such as diagnoses and care plans. Applicants have discovered that a primary reason prior efforts to automate the Physical exam have met with limited success is because of a failure to coordinate processing of anatomical data and diagnostic data. Descriptive health-related data can comprise an unlimited number of combinations of terms and is, therefore, inherently intractable. To handle descriptive health-related data, each individual medical practitioner develops his or her own preferred terminology and approach to recording the data, ranging from transcription to handwriting, to hiring staff to write or record for them. Automating such unruly data has not been efficient. Moreover, because of the wide variety of methods adopted by individual medical practitioners for handling such data, efforts to automate the collection of descriptive health-related data typically disrupt the established work flow patterns of the medical practitioners.
In a majority of EMR systems, efforts to integrate computer technology into the medical practitioner-patient encounter have largely focused on digitally recording the medical findings—learned during the Physical exam for later review, analysis or for electronic transmission and reproduction. These electronic medical records systems fail to facilitate the medical practitioner conducting the physical exam itself. Existing EMRs are highly structured to accommodate the complexity of medical practice, whereas medical practitioners' medical practices are typically highly individualized. The resulting conflict between personal work style and a structured electronic medical record system generally disrupts the physical exam, rather than facilitate it as intended. Because of these limitations, such systems have not gained wide acceptance in the medical community.
Some attempts have been made to computerize specific aspects of health care delivery apart from the clinical patient record. These limited attempts, or “point solutions,” include for example, expert systems that purportedly assist a medical practitioner in reaching a diagnosis or in selecting a proper drug dosage. Such systems are not popular with medical practitioners because, like the EMR systems, they disrupt the medical practitioners' workflow, thereby decreasing productivity. Moreover, medical practitioners typically do not require the assistance of an expert system to reach a diagnosis.
While offering enhanced capabilities, related medical electronic art, has proven to be less efficient than pen and paper. The medical practitioner's need for efficiency outweighs the need for improved functionality. Thus, the need for a system to electronically facilitate the medical practitioner's workday remains largely unfulfilled, and medical practitioners rely primarily on handwritten documentation.
In order to improve the efficiency of medical practitioners, several electronic medical records (EMR) systems have been created. These EMR systems allow medical practitioners to review records, document findings, and issue orders.
However, in the past, EMR systems were primarily used for data storage, record recall, and other operations at a time or place separate from the actual medical practitioner-patient encounter. The emergence of portable computer technology—including laptop computer devices, tablet form-factor devices such as the National Semiconductor WebPad demonstration system, and handheld form-factor devices such as the Palm, Inc. Palm V computer—allows medical practitioners to utilize EMR systems while working with patients. However, this mode of operation places a further premium on efficiency, since the user must quickly enter data as the patient describes problems or as the practitioner examines the patient. Thus, in constructing an EMR system for these new technologies, efficiency is paramount. A medical practitioner must be able to quickly select a data element to add to a medical record.
Some medical findings can only occur in one or a small number of locations on or in the body. An example is the finding “cardiovascular S3 present”, which identifies both the structure of the body that is affected and the problem with that structure. These medical findings are relatively easy to document efficiently with currently known methods. For example, a system may implement a process in which the user first selects the system of the body (“cardiovascular”) and then selects the finding (“S3 present”).
A number of EMR systems to document this type of finding exist. In such systems, a user typically selects a system and is presented with a list of findings for that system. For example, the Medcin system provides an interface of this type. This simple approach is feasible for this type of medical finding because the number of common medical findings per system is typically small enough to allow the list to fit on a single display and because once the medical finding is selected, there is no need to further specify the location of the body.
Unfortunately, it is difficult to document significantly more complex types of medical findings—medical findings for which both a medical problem and a body location must be documented and especially, in the case that location must be chosen from a wide range of possibilities. Examples may include many muskulo-skeletal system findings (e.g., sprains, fracture, or dislocation) or skin system medical findings (e.g., abrasion, laceration, or burn). The challenge to documenting such medical findings efficiently is that navigation through the space of options proceeds in two dimensions. Moreover, it could be even more difficult to provide a system that enables freedom to navigate, that makes it efficient to document a series of related medical problems in related locations of the body while making the whole documenting process relatively efficient.
Some EMR systems require a user to first specify a problem and then specify the location. A problem with these systems is that they make it difficult for a user to document multiple problems in the same location. On the other hand, some EMR systems attempt to document such findings by requiring a user to first specify a location and then specify the problem. However, for the types of medical findings under consideration, the number of locations may be too large and body location selection could be typically a multi-step process. For example, first a general region of the body is selected (e.g., left arm), then a more detailed location is selected (e.g., left wrist), finally a specific location is selected (e.g., left scaphoid bone). The difficulty is that the level of detail that a finding location is to be specified depends on the finding medical problem as well as other circumstances such as the user's preferences or judgement (e.g., one doctor may wish to indicate “cut on right hand” while another might document “cut from base of second finger to wrist of right hand” for the same injury), severity of the problem (a doctor might document the location of a deep cut more precisely than that of a shallow cut), clinic specialty (e.g., in a major trauma case, an emergency room doctor might document “left wrist fracture” and not provide additional details since the doctor's attention might be focused on more serious problems; later, an orthopedist treating the same patient might document “left scaphoid fracture”).
Consequently, at a given step of navigation, choosing a location might be intended to (a) drill down to provide options to select more detailed locations or (b) select the location as the terminal location of the problem. One approach to using a location-then-problem navigation method might require the user to first “drill down” to a picture that shows the location of the problem in the desired level of detail, to then explicitly exit “drill down” navigation mode to enter selection mode, to then select the location of the problem, and to finally select the problem. Note that if the user forgets to exit “drill down” mode, when the user selects a location, the system will drill down to a more detailed view, rather than selecting the location as the user intended. This extra step is thus both confusing and inefficient.
In a typical medical practice, it is common to add free text notation to findings using, for example, handwritten notes on a clipboard, dictated notes that are recorded and transcribed. However, current electronic medical records systems make such annotation difficult to perform efficiently because (a) this notation is not integrated into an efficient system for entering findings and (b) the means for entering these notations is cumbersome, involving free-end character-recognition text or recorded voice that is transcribed to text off line. What is needed is a system that integrates such nuanced descriptions into an electronic medical record for efficiently documenting findings or collections of findings at a location and that makes documenting complex annotations efficient by combining free-form input of text and graphics with (more efficient but less flexible) list selection input.
Moreover, it is desirable to document medical findings that require more detail than simply a problem, location, and state. In addition there is a need for a system and method to efficiently document multiple medical problems that are located near one another in the body. For example, an emergency room doctor treating a hand damaged in an industrial accident might need to document cuts to several fingers and the hand as well as broken bones in the several fingers and the hand. Such situations create a requirement for a method and system that efficiently coordinates navigation to different related body locations, selection of related body locations, and selection of multiple medical problems.